Fall 2026 Info

CC 410 - Advanced Programming - Fall 2026

Previous Versions

Instructor Contact Information

• Instructor: Mr. Russell Feldhausen (russfeld AT ksu DOT edu)
• Office: DUE 2213, but I mostly work remotely from Kansas City, MO
• Phone: (785) 292-3121 (Call/Text)
• Website: https://russfeld.me
• Virtual Office Hours: By appointment via Zoom. Book time to meet with me

Preferred Methods of Communication:

• Email: Students should email cc410-help (cc410-help@KSUemailProd.onmicrosoft.com). We will try to respond within one business day.
• Ed Discussion: For short questions and discussions of course content and assignments, Ed Discussion is preferred since questions can be asked once and answered for all students. Students are encouraged to post questions there and use that space for discussion, and the instructor will strive to answer questions there as well.
• Phone/Text: Emergencies only! We will do our best to respond as quickly as we can.

Prerequisites

• CC 310 - Data Structures & Algorithms I (taken on or after Fall 2024)
• CC 315 - Data Structures & Algorithms II (taken prior to Fall 2024)

Course Overview

Advanced programming techniques and projects. Concepts from object oriented programming, inheritance and polymorphism. GUI programming and event-driven programming. Software development methodologies, processes, and design patterns. Practical experience with professional communication and collaboration.

Course Description

In this course students gain experience writing programs using a variety of advanced programming techniques. Projects cover a variety of application domains and use a variety of technologies to help students master advanced computer programming concepts.

The goal is not just to write software that compiles without errors, but to develop well-written and maintainable software. This goal demands extra attention to design, documentation, and testing. Additionally, we will explore some of the powerful features of the various languages used, as well as other professional tools like Git.

Major Course Topics

• Software Development Practices
• Software Engineering Methodologies
• Design Patterns and Architectures
• Computer Security
• Advanced Object-Oriented Design
• GUI Programming
• Event-Driven Programming
• Professional Communication and Collaboration

Student Learning Outcomes

After completing this course, a successful student will be able to:

• Develop code following industry best-practices for code style and documentation
• Develop and execute unit tests that adequately test code for bugs and errors
• Make use of tools to determine the code coverage of a set of unit tests
• Make use of source code management tools to maintain and store a code base
• Create a class library following the object-oriented paradigm that makes effective use of inheritance and polymorphism where appropriate
• Develop a GUI for a given program that uses event-driven programming to respond to GUI events and manipulate underlying data models
• Apply common software development methodologies, processes and design patterns to create software that performs a desired task or solves a problem
• Communicate information about their code effectively with various audiences

Course Structure

These courses are being taught 100% online, and each module is self-paced. There may be some bumps in the road as we refine the overall course structure. Students will work at their own pace through a set of modules, with approximately one module being due each week. Material will be provided in the form of recorded videos, online tutorials, links to online resources, and discussion prompts. Each module will include a coding project or assignment, many of which will be graded automatically through Codio. Assignments may also include portions which will be graded manually via Canvas or other tools.

A common axiom in learner-centered teaching is “the person doing the work is the person doing the learning.” What this really means is that students primarily learn through grappling with the concepts and skills of a course while attempting to apply them. Simply seeing a demonstration or hearing a lecture by itself doesn’t do much in terms of learning. This is not to say that they don’t serve an important role - as they set the stage for the learning to come, helping you to recognize the core ideas to focus on as you work. The work itself consists of applying ideas, practicing skills, and putting the concepts into your own words.

The Work

There is no shortcut to becoming a great programmer. Only by doing the work will you develop the skills and knowledge to make you a successful computer scientist. This course is built around that principle, and gives you ample opportunity to do the work, with as much support as we can offer.

Tutorials, Quizzes & Examples: Each module will include many tutorial assignments, quizzes, and examples that will take you step-by-step through using a particular concept or technique. The point is not simply to complete the example, but to practice the technique and coding involved. You will be expected to implement these techniques on your own in the milestone assignment of the module - so this practice helps prepare you for those assignments.

Milestone Programming Assignments: Throughout the semester you will be building a non-trivial software project iteratively; every week a new milestone (a collection of features embodying a new version of a software application) will be due. Each milestone builds upon the prior milestone’s code base, so it is critical that you complete each milestone in a timely manner! This process also reflects the way software development is done in the real world - breaking large projects into more readily achievable milestones helps manage the development process.

Following along that real-world theme, programming assignments in this class will also be graded according to their conformance to coding style, documentation, and testing requirements. Each milestone’s rubric will include points assigned to each of these factors. It is not enough to simply write code that compiles and meets the specification; good code is readable, maintainable, efficient, and secure. The principles and practices of Object-Oriented programming that we will be learning in this course have been developed specifically to help address these concerns.

Concept Quizzes: There will be a couple of concept quizzes throughout the semester to check your understanding of various programming topics. These will allow you to demonstrate your problem-solving skills and your ability to apply what you’ve learned to novel situations.

Final Project: At the end of this course, you will design and develop a final project of your choosing to demonstrate your ability. This project can link back to your interest or other fields, and will serve as a capstone project for the Computational Core program.

Grading

In theory, each student begins the course with an A. As you submit work, you can either maintain your A (for good work) or chip away at it (for less adequate or incomplete work). In practice, each student starts with 0 points in the gradebook and works upward toward a final point total earned out of the possible number of points. In this course, each assignment constitutes a portion of the final grade, as detailed below:

• 10% - Tutorials & Quizzes
• 10% - Examples
• 40% - Programming Project Milestones
• 15% - Concept Quizzes
• 25% - Final Project

Up to 5% of the total grade in the class is available as extra credit. See the Extra Credit - Bug Bounty & Extra Credit - Helping Hands assignments for details.

Letter grades will be assigned following the standard scale:

• 90% - 100% → A
• 80% - 89.99% → B
• 70% - 79.99% → C
• 60% - 69.99% → D
• 00% - 59.99% → F

Submission, Regrading, and Early Grading Policy

As a rule, submissions in this course will not be graded until after they are due, even if submitted early. Students may resubmit assignments many times before the due date, and only the latest submission will be graded. For assignments submitted via GitHub release tag, only the tagged release that was submitted to Canvas will be graded, even if additional commits have been made. Students must create a new tagged release and resubmit that tag to have it graded for that assignment.

Once an assignment is graded, students are not allowed to resubmit the assignment for regrading or additional credit without special permission from the instructor to do so. In essence, students are expected to ensure their work is complete and meets the requirements before submission, not after feedback is given by the instructor during grading. However, students should use that feedback to improve future assignments and milestones.

For the programming project milestones, it is solely at the discretion of the instructor whether issues noted in the feedback for a milestone will result in grade deductions in a later milestones if they remain unresolved, though the instructor will strive to give students ample time to resolve issues before any additional grade deductions are made.

Likewise, students may ask questions of the instructor while working on the assignment and receive help, but the instructor will not perform a full code review nor give grading-level feedback until after the assignment is submitted and the due date has passed. Again, students are expected to be able to make their own judgments on the quality and completion of an assignment before submission.

That said, a student may email the instructor to request early grading on an assignment before the due date, in order to move ahead more quickly. The instructor’s receipt of that email will effectively mean that the assignment for that student is due immediately, and all limitations above will apply as if the assignment’s due date has now passed.

Collaboration Policy

In this course, all work submitted by a student should be created solely by the student without any outside assistance beyond the instructor and TA/GTAs. Students may seek outside help or tutoring regarding concepts presented in the course, but should not share or receive any answers, source code, program structure, or any other materials related to the course. Learning to debug coding problems is a vital skill, and students should strive to ask good questions and perform their own research instead of just sharing broken source code when asking for assistance.

Artificial Intelligence Usage Policy

This course uses a stoplight approach regarding the use of generative artificial intelligence (GenAI) tools, such as ChatGPT, Claude, Copilot, and others. Each assignment or group of assignments will be clearly labelled with one of three labels, indicating what level of GenAI usage is allowed.

Details

RED: GenAI Prohibited You may not use any GenAI tools to complete this assignment. This assignment's main goal is to develop your own skills related to a particular task or topic, or to assess your own understanding of the concepts and skills required for this course. GenAI tools are therefore prohibited for these assignments, as they do not reflect or enhance your own learning journey.

Policy Violations: Any usage of GenAI for this assignment will be treated as a violation of the K-State Honor Pledge and may result in a grade of 0 for the assignment and other sanctions approved through the K-State Honor Council.

Details

YELLOW: Limited GenAI Usage Allowed You may use GenAI tools in a limited way to complete this assignment. The assignment description may provide additional information about what tools are allowed and how they can be used. The goal of this assignment is to allow you to work with GenAI to complete a task or achieve a goal, but the completed work should still be a majority your own effort.

Citations Required: Any usage of GenAI must be noted and cited directly in the work, either in source code comments or text citations in written work. Citations should include the tool used, the prompt(s) given, context provided to the tool (e.g. existing code), and a discussion of how the results were used to complete the assignment.

No Direct AI Results: For this assignment, you may not include the GenAI results directly in your submission - it must be used to inform and adapted to fit your own work. For example, you may not prompt GenAI tools to just write your code and submit that directly; instead, you should ask for help performing specific tasks and then use the results within your own work.

Understand Your Work: To ensure compliance with this policy, the instructor reserves the right to request additional discussion or explanation of any work submitted by a student. The student should understand and be able to clearly explain all submitted work and code, even materials directly or indirectly produced by GenAI. A student who is unable to explain a submission to the satisfaction of the instructor may be considered to be in violation of this policy.

Policy Violations: Any usage of GenAI that involves direct submission of the GenAI outputs without additional work done by the student, or use of GenAI without proper citation, may be treated as a violation of the K-State Honor Pledge and may result in a grade of 0 for the assignment and other sanctions approved through the K-State Honor Council.

Details

GREEN: GenAI Encouraged You may make unlimited use of GenAI tools to complete this assignment. The goal of this assignment is to ensure you are comfortable with using GenAI tools to specifically meet a need or achieve a goal.

Citations Required: Any usage of GenAI must be noted and cited directly in the work, either in source code comments or text citations in written work. Citations should include the tool used, the prompt(s) given, context provided to the tool (e.g. existing code), and a discussion of how the results were used to complete the assignment.

Direct AI Results Allowed: For this assignment, you may include the GenAI results directly in your submission. It is still your responsibility to ensure the submission meets the assignment’s goals and is correct and factual - remember that GenAI is not infallible and may produce incorrect results. You are still solely responsible for ensuring the submission meets the stated assignment goals, and assignments in this category may receive additional scrutiny for correctness and accuracy.

Understand Your Work: To ensure compliance with this policy, the instructor reserves the right to request additional discussion or explanation of any work submitted by a student. The student should understand and be able to clearly explain all submitted work and code, even materials directly or indirectly produced by GenAI. A student who is unable to explain a submission to the satisfaction of the instructor may be considered to be in violation of this policy.

Policy Violations: Any usage of GenAI without proper citation may be treated as a violation of the K-State Honor Pledge and may result in a grade of 0 for the assignment and other sanctions approved through the K-State Honor Council.

Please contact the instructor if you have any questions about this GenAI policy. It is your responsibility to understand it and proactively ask questions if you are unsure; ignorance of this policy is not an excuse for violating it.

Artificial Intelligence Disclosure

In keeping with the expectation for transparency and citation regarding the use of generative artificial intelligence (GenAI), the instructors of this course will clearly denote any usage of GenAI tools in the process of teaching this class. Specific policies for the usage of GenAI by the instructors and TAs of this course are given below:

• RED: GenAI Prohibited Student Communication - GenAI will never be used to when communicating with students. We believe it is important for students to receive real, authentic communication from instructors and TAs.
• RED: GenAI Prohibited Grading - GenAI will never be used to suggest or assign grades for any student-submitted work. All grading decisions will be done solely by instructors and TAs.
• YELLOW: Limited GenAI Usage Allowed Code Review & Feedback - Instructors may use GenAI tools to assist with code reviews due to the scope and scale of these projects. However, all feedback given will be “human in the loop” with the final feedback coming directly from the instructors and TAs (see the first point above).
• YELLOW: Limited GenAI Usage Allowed Lesson & Learning Content - GenAI may be used in a limited way to construct lessons and learning content, such as homework scenarios or simple graphics. All usage of GenAI will be clearly marked and cited. (As of August 2026, no GenAI content exists in the course).

Late Work

Since this course is entirely online, students may work at any time and at their own pace through the modules. However, to keep everyone on track, there will be approximately one module due each week. Each graded item in the module will have a specific due date specified. Any assignment submitted late will have that assignment’s grade reduced by 10% of the total possible points on that project for each day it is late. This penalty will be assessed automatically in the Canvas gradebook. For the purposes of record keeping, a combination of the time of a submission via Canvas and the creation of a release in GitHub will be used to determine if the assignment was submitted on time.

However, even if a module is not submitted on time, it must still be completed before a student is allowed to begin the next module. So, students should take care not to get too far behind, as it may be very difficult to catch up.

Finally, all course work must be submitted on or before the last day of the semester in which the student is enrolled in the course in order for it to be graded on time.

If you have extenuating circumstances, please discuss them with the instructor as soon as they arise so other arrangements can be made. If you find that you are getting behind in the class, you are encouraged to speak to the instructor for options to make up missed work.

Incomplete Policy

Students should strive to complete this course in its entirety before the end of the semester in which they are enrolled. However, since retaking the course would be costly and repetitive for students, we would like to give students a chance to succeed with a little help rather than immediately fail students who are struggling.

If you are unable to complete the course in a timely manner, please contact the instructor to discuss an incomplete grade. Incomplete grades are given solely at the instructor’s discretion. See the official K-State Grading Policy for more information. In general, poor time management alone is not a sufficient reason for an incomplete grade.

Unless otherwise noted in writing on a signed Incomplete Agreement Form, the following stipulations apply to any incomplete grades given in this course:

1. Students who request an incomplete will have their final grade capped at a C.
2. Students will be given a maximum of 8 calendar weeks from the end of the enrolled semester to complete the course. It is expected that students have completed at least half of the course in order to qualify for an incomplete.
3. Students understand that access to instructor and TA assistance may be limited after the end of an academic semester due to holidays and other obligations.
4. Any modules in a future CC course which depend on incomplete work will not be accessible until the previous course is finished
   1. For example, if a student is given an incomplete in CC 210, then all modules in CC 310 will be inaccessible until CC 210 is complete
5. If a student fails to resolve an incomplete grade after 8 weeks, they will be assigned an ‘F’ in the course. In addition, they will be dropped from any other Computational Core courses which require the failed course as a prerequisite or corequisite.

Recommended Texts & Supplies

To participate in this course, students must have access to a modern web browser and broadband internet connection. All course materials will be provided via Canvas and Codio. Modules may also contain links to external resources for additional information, such as programming language documentation.

Students will make use of GitHub for source code management.

Students may choose to do some development work on their own computer. The recommended software is Visual Studio Code along with access to a system running Ubuntu. For Windows systems, Ubuntu can be installed via the Windows Subsystem for Linux. For Mac systems, Ubuntu can be installed in a virtual machine through VirtualBox.

Subject to Change

The details in this syllabus are not set in stone. Due to the flexible nature of this class, adjustments may need to be made as the semester progresses, though they will be kept to a minimum. If any changes occur, the changes will be posted on the Canvas page for this course and emailed to all students. All changes may also be posted to Canvas.

Standard Syllabus Statements

Netiquette

Online communication is inherently different than in-person communication. When speaking in person, many times we can take advantage of the context and body language of the person speaking to better understand what the speaker means, not just what is said. This information is not present when communicating online, so we must be much more careful about what we say and how we say it in order to get our meaning across.

Here are a few general rules to help us all communicate online in this course, especially while using tools such as Canvas or Discord:

• Use a clear and meaningful subject line to announce your topic. Subject lines such as “Question” or “Problem” are not helpful. Subjects such as “Logic Question in Project 5, Part 1 in Java” or “Unexpected Exception when Opening Text File in Python” give plenty of information about your topic.
• Use only one topic per message. If you have multiple topics, post multiple messages so each one can be discussed independently.
• Be thorough, concise, and to the point. Ideally, each message should be a page or less.
• Include exact error messages, code snippets, or screenshots, as well as any previous steps taken to fix the problem. It is much easier to solve a problem when the exact error message or screenshot is provided. If we know what you’ve tried so far, we can get to the root cause of the issue more quickly.
• Consider carefully what you write before you post it. Once a message is posted, it becomes part of the permanent record of the course and can easily be found by others.
• If you are lost, don’t know an answer, or don’t understand something, speak up! Email and Canvas both allow you to send a message privately to the instructors, so other students won’t see that you asked a question. Don’t be afraid to ask questions anytime, as you can choose to do so without any fear of being identified by your fellow students.
• Class discussions are confidential. Do not share information from the course with anyone outside of the course without explicit permission.
• Do not quote entire message chains; only include the relevant parts. When replying to a previous message, only quote the relevant lines in your response.
• Do not use all caps. It makes it look like you are shouting. Use appropriate text markup (bold, italics, etc.) to highlight a point if needed.
• No feigning surprise. If someone asks a question, saying things like “I can’t believe you don’t know that!” are not helpful, and only serve to make that person feel bad.
• No “well-actually’s.” If someone makes a statement that is not entirely correct, resist the urge to offer a “well, actually…” correction, especially if it is not relevant to the discussion. If you can help solve their problem, feel free to provide correct information, but don’t post a correction just for the sake of being correct.
• Do not correct someone’s grammar or spelling. Again, it is not helpful, and only serves to make that person feel bad. If there is a genuine mistake that may affect the meaning of the post, please contact the person privately or let the instructors know privately so it can be resolved.
• Avoid subtle -isms and microaggressions. Avoid comments that could make others feel uncomfortable based on their personal identity. See the syllabus section on Diversity and Inclusion above for more information on this topic. If a comment makes you uncomfortable, please contact the instructor.
• Avoid sarcasm, flaming, advertisements, lingo, trolling, doxxing, and other bad online habits. They have no place in an academic environment. Tasteful humor is fine, but sarcasm can be misunderstood.

As a participant in course discussions, you should also strive to honor the diversity of your classmates by adhering to the K-State Principles of Community.

SafeZone Ally

I am part of the SafeZone community network of trained K-State faculty/staff/students who are available to listen and support you. As a SafeZone Ally, I can help you connect with resources on campus to address problems you face that interfere with your academic success, particularly issues of sexual violence, hateful acts, or concerns faced by individuals due to sexual orientation/gender identity. My goal is to help you be successful and to maintain a safe and equitable campus.

Copyright Notice

© The materials in this online course fall under the protection of all intellectual property, copyright and trademark laws of the U.S. The digital materials included here come with the legal permissions and releases of the copyright holders. These course materials should be used for educational purposes only; the contents should not be distributed electronically or otherwise beyond the confines of this online course. The URLs listed here do not suggest endorsement of either the site owners or the contents found at the sites. Likewise, mentioned brands (products and services) do not suggest endorsement. Students own copyright to what they create.